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PEFT- Adapters "]},{"cell_type":"code","source":["%%capture\n","!pip install datasets==2.12.0 adapter-transformers==3.2.1"],"metadata":{"id":"sdqUgbnviiz6","executionInfo":{"status":"ok","timestamp":1685114424008,"user_tz":-180,"elapsed":15284,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["!pip list |egrep adapter-transformers\n","!pip list |egrep ^datasets"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HlOdIGbrlvJ0","executionInfo":{"status":"ok","timestamp":1685046389170,"user_tz":-180,"elapsed":2758,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"9e48b4e9-c520-4a9f-a02f-3b1fe6ae4b50"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["adapter-transformers          3.2.1\n","datasets                      2.12.0\n"]}]},{"cell_type":"markdown","metadata":{"id":"tszbPHwjFfkP"},"source":["## Preparation for Google Collab"]},{"cell_type":"code","metadata":{"id":"BRzRpdbLDDKv","executionInfo":{"status":"ok","timestamp":1685114451849,"user_tz":-180,"elapsed":8473,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"source":["import torch, os\n","from torch import cuda\n","import numpy as np\n","# transformers\n","from transformers import AdapterTrainer\n","from transformers import (BertTokenizerFast, \n","                          BertForSequenceClassification)\n","from transformers import Trainer, TrainingArguments\n","from datasets import load_dataset\n","\n","device = 'cuda' if cuda.is_available() else 'cpu'"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BZv1tmcx_pNo"},"source":["### Loading pre-trained model and tokenizer"]},{"cell_type":"code","metadata":{"id":"DE1Rwh2BtnwT","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["97f233090f234bacbcf51be02160c959","cb483deb4bee49c2805efd0f2171b96a","5a2104f16629480094bfe1a5d58113d8","13f14e38268845e89a66e4a6a7dc5a1b","9322b41f09f941339a860077ed2d2379","e533a06bf51c4220a3962723e400a80e","0752ffc73fa048d6a5b4c1018677d16e","bb2bd9f0ea6c4305bf038b711aa47d3c","540f7db653384b53a06b870a43807e52","08dd2055d6a34e0bb8bd168188b80550","23f029918e484c17ba020cc311739c45","f154890f2e544ae6a89cb3167353c9aa","b81fb1918c384cd38e6a7be676b6e179","fa8a2ada34d44615b6166bddbd81d57b","b058f6e24846497894903c247ea1560f","15d80b71eb964c3cbafb401a4639ec94","6054bc084dee4a4499008e0933dbde7e","dcbfe223960341b5afdf9150a514582d","538f5e342c02437ab184e75fd8a41480","0566d8ce8fe1451d8f96f7874f94d87a","6d4502a3e9854b68b0685f6c2a69e533","3e18b495a727446295bb0432e9ea9e8f","ef8f2409298b4413b6c2ccebfe87d04c","6f818e40111243269d93f076dcc5e303","e0e9f4c18c554f64ac0125589f819781","fdd45c98207f4174bfcd97ab8ac1a745","ede93876e7484a769ee99d79e89bf642","399dd5c8bada4c3ead26b3f3e54e751a","6bafeaab3e3d45a0853cc6dd1ebd0938","1ed87e2fab424c3f8a829ecc8311e041","058aec1e69104d89b6f50832b6158acc","2cc230a2a92d48778c3139b02cd08796","05a3a9492be44dcf96fcfa5906613ccb","7b87656f7f03497cb1c69ff48e24d7ce","c503012aca6e47b9ad8b06264d4213de","54804f0eeb2d4743bdbf73d649397a87","d434e0b9ae834c5cac45e5bcd6c27780","57f11130b33f4cbf913281997e8f429b","84c7b60e7055475986be168e8fbc9e1c","8db69cddd1374fc49ba25120ec0f9043","ee49bf78fe7c4deeaa0d7d712d8bb764","ffcc133a1c7544fe93e5116f9febfd72","8504b5471f2840c59a5fc1d7cf6e26c6","fc51963999734b6b8f603cd89f4be840"]},"executionInfo":{"status":"ok","timestamp":1685114459579,"user_tz":-180,"elapsed":2289,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"b22856e7-26bc-4431-a669-913d0dd79c6e"},"source":["model_path= 'bert-base-uncased'\n","tokenizer = BertTokenizerFast.from_pretrained(model_path)"],"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"97f233090f234bacbcf51be02160c959"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f154890f2e544ae6a89cb3167353c9aa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, 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imdb_val.shape"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_kfFp7nkbki4","executionInfo":{"status":"ok","timestamp":1685114886025,"user_tz":-180,"elapsed":310,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"9ba17c8b-b6ec-43be-961e-6fedad57955d"},"source":["imdb_train.shape, imdb_test.shape, imdb_val.shape"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((4000, 2), (1000, 2), (1000, 2))"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":72,"referenced_widgets":["f01237a9bed4477e8e389a42991c691f","cde263786f494aa3ba3060ec74d063ae","96a4d61a21ae4108b373822ad179f190","7e243c22b84e4b31b504deb134f24135","a7b5d7139f5e47f7942db9c1ed0852cb","e3382cff91e64ee7a406ce31afa3ad42","a77c27a8f0de4dd4b6ac324ab4871ea8","a5a47f80bbcd42c4b00319a211d55752","a06f0656c7ae45c4aca2d99b087672ae","342ea3864c394684a56bdabc8666df24","4ecded4862594fd3b614de15085f909a"]},"id":"WpuCsmUVXgm9","executionInfo":{"status":"ok","timestamp":1685114889254,"user_tz":-180,"elapsed":1958,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"29f6c615-3279-4d22-a5b5-fb7da72a6480"},"source":["def tokenize_it(e):\n","  return tokenizer(e['text'], \n","                   padding=True, \n","                   truncation=True)\n","\n","enc_train=imdb_train.map(tokenize_it, batched=True, batch_size=1000)\n","enc_test=imdb_test.map(tokenize_it, batched=True, batch_size=1000) \n","enc_val=imdb_val.map(tokenize_it, batched=True, batch_size=1000)"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/4000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f01237a9bed4477e8e389a42991c691f"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0/cache-c2d9877f3531efc8.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0/cache-fcdd0fe945c92e84.arrow\n"]}]},{"cell_type":"markdown","source":["# Loading model for fine-tuning"],"metadata":{"id":"hMNfQSNPBwlB"}},{"cell_type":"markdown","metadata":{"id":"ACNK9ZQKD4mt"},"source":["## Preparing training settings with TrainingArguments and Trainer class\n","\n","TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop\n","itself**."]},{"cell_type":"code","metadata":{"id":"OZD1jnhFWEd5","executionInfo":{"status":"ok","timestamp":1685114892176,"user_tz":-180,"elapsed":402,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"source":["training_args = TrainingArguments(\n","    \"/tmp\",\n","    do_train=True,\n","    do_eval=True,\n","    num_train_epochs=3,\n","    learning_rate=2e-4,              \n","    per_device_train_batch_size=16,  \n","    per_device_eval_batch_size=16,\n","    warmup_steps=100,                \n","    weight_decay=0.01,\n","    evaluation_strategy=\"epoch\",\n","    save_strategy=\"epoch\",\n","    fp16=True,\n","    load_best_model_at_end=True\n",")"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HUn5uFHkbPEb"},"source":["Let's design our evaluation metrics as follows:"]},{"cell_type":"code","metadata":{"id":"8Ct3v6RZZSmj","executionInfo":{"status":"ok","timestamp":1685114895048,"user_tz":-180,"elapsed":3,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"source":["\n","def compute_acc(p):\n","  preds = np.argmax(p.predictions, axis=1)\n","  acc={\"Accuracy\": (preds == p.label_ids).mean()}\n","  return acc"],"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":["# PEFT-adapters"],"metadata":{"id":"EC4UeGqf2Sbq"}},{"cell_type":"code","source":["from transformers import BertModelWithHeads\n","model = BertModelWithHeads\\\n","    .from_pretrained(model_path)"],"metadata":{"id":"ADe3HWSAGenN","colab":{"base_uri":"https://localhost:8080/","height":190,"referenced_widgets":["7f235736fbcf44aaa3104d114850700f","ab86ae33aa784d59b19f6e87c78c2b61","9d6877a142504fde9dd4ca5e516608f6","cf52a493dd7a4a5fb7634d69bb84ac80","4479ddc339e54a85afb0b73153e34782","2426cfcc509548fc931d8571bab2361e","199411eee21543d598b43ea1abc8a8c3","54b6a2428c154c5bac27f4f5a5da2393","c808ee9df3604c6a8430ace40330ad6c","2909bf79319c4db5a64f78b466fe89d3","42a4f96b761e482b814bc3104de77425"]},"executionInfo":{"status":"ok","timestamp":1685114904444,"user_tz":-180,"elapsed":7921,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"edd9b9a3-85fe-4c44-ebda-e9153b991a25"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/adapters/models/bert/adapter_model.py:269: FutureWarning: This class has been renamed to `BertAdapterModel` in v3. Please use the new class instead as this class might be removed in a future version.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f235736fbcf44aaa3104d114850700f"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/adapters/models/bert/adapter_model.py:247: FutureWarning: This class has been renamed to `BertAdapterModel` in v3. Please use the new class instead as this class might be removed in a future version.\n","  warnings.warn(\n","Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModelWithHeads: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight']\n","- This IS expected if you are initializing BertModelWithHeads from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertModelWithHeads from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]}]},{"cell_type":"code","source":["# we add an adapter and named imdb_sentiment\n","model.add_adapter(\"imdb_sentiment\")\n","# we add a classification head and asscociate it with added adapter\n","model.add_classification_head(\n","    \"imdb_sentiment\",\n","    num_labels=2)\n","\n","# we tell the training process that added adpater will be trained!\n","model.train_adapter(\"imdb_sentiment\")"],"metadata":{"id":"ymgvc1NqGfZ1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"L9SACBDRG1UQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"ZBy29BO62_dd","executionInfo":{"status":"ok","timestamp":1685115266644,"user_tz":-180,"elapsed":350,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"KlePfD7wHGlg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# we count them in Millions\n","trainable_params=model.num_parameters(only_trainable=True)/(2**20) \n","all_params=model.num_parameters() /2**20\n","print(f\"{all_params=:.2f} M\\n\"+\n","      f\"{trainable_params=:.2f} M\\n\"+\n","      f\"The efficiency ratio is \\\n","      {100*trainable_params/all_params:.2f}%\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MFsdCmonHeo0","executionInfo":{"status":"ok","timestamp":1685123561102,"user_tz":-180,"elapsed":341,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"25613f95-5e66-469f-8d68-4513d3a1fa4e"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["all_params=105.83 M\n","trainable_params=1.42 M\n","The efficiency ratio is       1.34%\n"]}]},{"cell_type":"code","source":["\n","trainer = AdapterTrainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=enc_train,\n","    eval_dataset=enc_val,\n","    compute_metrics=compute_acc,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eeZl4PRK2_bG","executionInfo":{"status":"ok","timestamp":1685115292939,"user_tz":-180,"elapsed":6842,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"375f965c-8657-44c9-b240-375fdadc1ed6"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stderr","text":["Using cuda_amp half precision backend\n"]}]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"id":"0vWYAqns2_Wp","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1685115391242,"user_tz":-180,"elapsed":98309,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"51d8d382-5831-41df-c1fb-010a686ae3c8"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stderr","text":["The following columns in the training set don't have a corresponding argument in `BertModelWithHeads.forward` and have been ignored: text. If text are not expected by `BertModelWithHeads.forward`,  you can safely ignore this message.\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n","***** Running training *****\n","  Num examples = 4000\n","  Num Epochs = 3\n","  Instantaneous batch size per device = 16\n","  Total train batch size (w. parallel, distributed & accumulation) = 16\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 750\n","  Number of trainable parameters = 1486658\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='750' max='750' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [750/750 01:34, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>0.261302</td>\n","      <td>0.890000</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.317400</td>\n","      <td>0.252638</td>\n","      <td>0.895000</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.317400</td>\n","      <td>0.248115</td>\n","      <td>0.911000</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The following columns in the evaluation set don't have a corresponding argument in `BertModelWithHeads.forward` and have been ignored: text. If text are not expected by `BertModelWithHeads.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 1000\n","  Batch size = 16\n","Saving model checkpoint to /tmp/checkpoint-250\n","Configuration saved in /tmp/checkpoint-250/imdb_sentiment/adapter_config.json\n","Module weights saved in /tmp/checkpoint-250/imdb_sentiment/pytorch_adapter.bin\n","Configuration saved in /tmp/checkpoint-250/imdb_sentiment/head_config.json\n","Module weights saved in /tmp/checkpoint-250/imdb_sentiment/pytorch_model_head.bin\n","Configuration saved in /tmp/checkpoint-250/imdb_sentiment/head_config.json\n","Module weights saved in /tmp/checkpoint-250/imdb_sentiment/pytorch_model_head.bin\n","The following columns in the evaluation set don't have a corresponding argument in `BertModelWithHeads.forward` and have been ignored: text. If text are not expected by `BertModelWithHeads.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 1000\n","  Batch size = 16\n","Saving model checkpoint to /tmp/checkpoint-500\n","Configuration saved in /tmp/checkpoint-500/imdb_sentiment/adapter_config.json\n","Module weights saved in /tmp/checkpoint-500/imdb_sentiment/pytorch_adapter.bin\n","Configuration saved in /tmp/checkpoint-500/imdb_sentiment/head_config.json\n","Module weights saved in /tmp/checkpoint-500/imdb_sentiment/pytorch_model_head.bin\n","Configuration saved in /tmp/checkpoint-500/imdb_sentiment/head_config.json\n","Module weights saved in /tmp/checkpoint-500/imdb_sentiment/pytorch_model_head.bin\n","The following columns in the evaluation set don't have a corresponding argument in `BertModelWithHeads.forward` and have been ignored: text. If text are not expected by `BertModelWithHeads.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 1000\n","  Batch size = 16\n","Saving model checkpoint to /tmp/checkpoint-750\n","Configuration saved in /tmp/checkpoint-750/imdb_sentiment/adapter_config.json\n","Module weights saved in /tmp/checkpoint-750/imdb_sentiment/pytorch_adapter.bin\n","Configuration saved in /tmp/checkpoint-750/imdb_sentiment/head_config.json\n","Module weights saved in /tmp/checkpoint-750/imdb_sentiment/pytorch_model_head.bin\n","Configuration saved in /tmp/checkpoint-750/imdb_sentiment/head_config.json\n","Module weights saved in /tmp/checkpoint-750/imdb_sentiment/pytorch_model_head.bin\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n","Loading best adapter(s) from /tmp/checkpoint-750 (score: 0.24811480939388275).\n","Loading module configuration from /tmp/checkpoint-750/imdb_sentiment/adapter_config.json\n","Overwriting existing adapter 'imdb_sentiment'.\n","Loading module weights from /tmp/checkpoint-750/imdb_sentiment/pytorch_adapter.bin\n","Loading module configuration from /tmp/checkpoint-750/imdb_sentiment/head_config.json\n","Overwriting existing head 'imdb_sentiment'\n","Adding head 'imdb_sentiment' with config {'head_type': 'classification', 'num_labels': 2, 'layers': 2, 'activation_function': 'tanh', 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, 'use_pooler': False, 'bias': True}.\n","Loading module weights from /tmp/checkpoint-750/imdb_sentiment/pytorch_model_head.bin\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=750, training_loss=0.27304420979817706, metrics={'train_runtime': 98.1022, 'train_samples_per_second': 122.321, 'train_steps_per_second': 7.645, 'total_flos': 3212080128000000.0, 'train_loss': 0.27304420979817706, 'epoch': 3.0})"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["import pandas as pd\n","q=[trainer.evaluate(eval_dataset=data) for data in [enc_train, enc_val, enc_test]]\n","pd.DataFrame(q, index=[\"train\",\"val\",\"test\"]).iloc[:,:5]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"XSpIAMslCY7H","executionInfo":{"status":"ok","timestamp":1685119089197,"user_tz":-180,"elapsed":19618,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"dcf26e97-cf9b-4ccc-cb06-03a7d4e67ffd"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["The following columns in the evaluation set don't have a corresponding argument in `BertModelWithHeads.forward` and have been ignored: text. If text are not expected by `BertModelWithHeads.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 4000\n","  Batch size = 16\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='752' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [250/250 01:12]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The following columns in the evaluation set don't have a corresponding argument in `BertModelWithHeads.forward` and have been ignored: text. If text are not expected by `BertModelWithHeads.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 1000\n","  Batch size = 16\n","The following columns in the evaluation set don't have a corresponding argument in `BertModelWithHeads.forward` and have been ignored: text. If text are not expected by `BertModelWithHeads.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 1000\n","  Batch size = 16\n"]},{"output_type":"execute_result","data":{"text/plain":["       eval_loss  eval_Accuracy  eval_runtime  eval_samples_per_second  \\\n","train   0.151642        0.94575       13.0559                  306.374   \n","val     0.248115        0.91100        3.2480                  307.885   \n","test    0.219320        0.91900        3.2568                  307.052   \n","\n","       eval_steps_per_second  \n","train                 19.148  \n","val                   19.397  \n","test                  19.344  "],"text/html":["\n","  <div id=\"df-27c57523-7b63-4054-b465-f273f5f9e4da\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>eval_loss</th>\n","      <th>eval_Accuracy</th>\n","      <th>eval_runtime</th>\n","      <th>eval_samples_per_second</th>\n","      <th>eval_steps_per_second</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>train</th>\n","      <td>0.151642</td>\n","      <td>0.94575</td>\n","      <td>13.0559</td>\n","      <td>306.374</td>\n","      <td>19.148</td>\n","    </tr>\n","    <tr>\n","      <th>val</th>\n","      <td>0.248115</td>\n","      <td>0.91100</td>\n","      <td>3.2480</td>\n","      <td>307.885</td>\n","      <td>19.397</td>\n","    </tr>\n","    <tr>\n","      <th>test</th>\n","      <td>0.219320</td>\n","      <td>0.91900</td>\n","      <td>3.2568</td>\n","      <td>307.052</td>\n","      <td>19.344</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27c57523-7b63-4054-b465-f273f5f9e4da')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-27c57523-7b63-4054-b465-f273f5f9e4da button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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